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Direct supervisor: Marketa Zvelebil, PhD
Location: Ludwig Institute for Cancer Research – UCL Branch
Descriptive title of research activity: Modelling of pathways triggered by GPCRs
Overall goals: The mathematical modelling and analysis of pathways triggered by GPCRs in inflammation and migration.
Rationale and Significance: Signalling pathways are the way by which cells communicate with their environment and with each other. Understanding the operation of signalling networks in the living cell is a major challenge in post-genomic molecular biology. Construction of accurate models of biochemical signalling pathways can be achieved through a series of successive steps. The first step is to define the model system as accurately and fully as possible. This involves the construction of a connection map (or a pathway diagram) based on available protein-protein, protein-ligand and biological data. The second step is to collect parameter information from existing biochemical literature and experimental data that will describe the dynamics of the model. The dynamic model is used to model the behaviour of the specific pathway, to suggest experimental results and to verify and explain experimental observations.
Description of work: The student will initially finalize a static network for GPCR pathways activated by chemokines using information from literature and other members of the MAIN consortium. This will include mechanisms for down-regulation of GPCR signalling, such as phosphorylation and internalization. Subsequently, based on the availability of biochemical data (such as concentrations and time-lapsed reaction profiles) either a model using differential equations or a stochastic model will be designed. To do this the student will use in-house database/knowledge bases and programs such as IPPI, Cluster and outside programs such as MatLab for mathematical modelling, Gepasi, JDesigner, and Jarnac. The student will also be expected to perform some laboratory work during their final year to verify some of their predictions. This work will be supervised by Dr Anne Ridley (LICR-UCL)
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